Many fields, such as clinical neurology, neurosurgery, and neuropsychology, as well as basic neuroscience research, require methods for detecting, localizing, characterizing, and (in some situations) predicting, normal and abnormal brain function. Since the late nineteenth century, invasive and noninvasive recordings of the electromagnetic (EM) activity resulting from brain function have been used for these purposes.
Brain function produces electrical and magnetic (EM) activity that can be recorded from the scalp (electroencephalographic activity (EEG)), from the surface of the brain (electrocorticographic activity (ECoG)), or from within the brain. This activity can be expressed in various ways, such as, for example, oscillations at different frequencies, voltage levels, or firing frequencies of individual cortical neurons. For many decades, these measures of brain function have been used clinically (e.g., to localize mass lesions such as tumors, to recognize seizures, to assess auditory, visual, and somatosensory pathways) (see generally Karbowski, Eur Neurol 30:170-175, 1990). They have also been used experimentally in both humans and animals to explore the anatomical and physiological bases of normal and abnormal brain function. At present, EM methods continue to be a mainstay of both clinical assessments and research explorations of brain function.
Yet, current approaches to studying brain function with any sensor modality (e.g., functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), or single neuron recordings) are constrained by the fact that they are typically retrospective. This is because the brain signal change that is associated with a specific function or event is either unknown a priori or does not sufficiently generalize to a different individual to permit prospective (i.e., real-time) extraction of that signal with the requisite fidelity. Also, enormous inter- and intra-individual variations exist in the anatomical and physiological features of brain functions and in the EM activity that is associated with these functions. These serious limitations have prohibited prospective study of many important problems.
The requirement for extensive data collection under defined conditions and subsequent detailed analyses makes it difficult or impossible to use EM methods for a wide variety of important clinical and research purposes. As a result, only the grossest most obvious features of EM activity can be confidently used to assess, in a predefined standard way, brain function in individuals (e.g., the total absence of EEG for a defined period indicates brain death). In contrast, other clinical and experimental uses of EM activity typically require laborious collection of often extensive EM data under defined conditions from each individual, and subsequent elaborate analysis of these data to determine for that individual the relationships between specific aspects of brain function and specific features of EM activity.
Thus, for example, using EEG or ECoG to predict seizures in a person with epilepsy has only been possible after extensive study had defined for that person the exact differences (e.g., in frequency spectra) between the EEG or ECoG activity that preceded a seizure and the activity at other times.
As another example, using EEG to monitor state of alertness in a specific person is only possible after extensive study has defined the precise relationships between specific EEG features and specific states of alertness for that person.
The identification of cortical areas critical for specific functions such as speech is an essential prerequisite in surgery to remove epileptic foci, tumors, or vascular malformations. This identification currently requires difficult, repeated, and often traumatic electrical stimulation (ECS) of cortex (see e.g., Branco et al., J Clin Neurophysiol 20:17-25, 2003; Keles, J Neurosurg 100:369-375, 2004). ECS is coarse in its ability to delineate regions of motor and speech cortex and carries risks of inducing seizures or misidentifying critical areas. The capacity to passively locate spatially localized changes produced by movements and language would be a valuable neurosurgical tool that could augment or even replace current methods to locate function, such as ECS. However, these functional changes often manifest themselves in signal features that differ markedly from individual to individual, which prohibits the real-time detection of these changes by current methods. A passive method for identifying the cortical areas critical for specific functions, such as speech, could reduce the need for ECS and improve accuracy, but only if it did not entail prolonged data collection and subsequent analysis.
As described above, the requirement for extensive data collection under defined conditions and subsequent detailed analyses makes it difficult or impossible to use EM methods for a wide variety of important clinical and research purposes.
These same constraints exist for any complex system by which multiple signals are generated. Money markets, stock markets, astronomical phenomena, weather, or complex communication systems (e.g., level of chatter on the Internet) all require the same level of extensive retrospective analyses, thereby often eliminating the possibility of detecting or predicting significant events in a prospective fashion. A need exists for methods for detecting and analyzing signals of true events in such systems in real time.